G9a in Cancer: Mechanisms, Therapeutic Advancements, and Clinical Implications
Bibliographic record
Abstract
G9a, also named EHMT2, is a histone 3 lysine 9 (H3K9) methyltransferase responsible for catalyzing H3K9 mono- and dimethylation (H3K9me1 and H3K9me2). G9a contributes to various aspects of embryonic development and tissue differentiation through epigenetic regulation. Furthermore, the aberrant expression of G9a is frequently observed in various tumors, particularly in prostate cancer, where it contributes to cancer pathogenesis and progression. This review highlights the critical role of G9a in multiple cancer-related processes, such as epigenetic dysregulation, tumor suppressor gene silencing, cancer lineage plasticity, hypoxia adaption, and cancer progression. Despite the increased research on G9a in prostate cancer, there are still significant gaps, particularly in understanding its interactions within the tumor microenvironment and its broader epigenetic effects. Furthermore, this review discusses the recent advancements in G9a inhibitors, including the development of dual-target inhibitors that target G9a along with other epigenetic factors such as EZH2 and HDAC. It aims to bring together the existing knowledge, identify gaps in the current research, and suggest future directions for research and treatment strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".